The overall goal of this project is to understand the formation and behavior of urban heat islands and to mitigate their effects through sensible city engineering and design practices. The three objectives of this research project are:
Quantify the factors that contribute to urban heat island development and behavior for the 100 largest metropolitan areas on the planet
A comprehensive analysis of the annual, seasonal, and diurnal behavior of urban heat islands is being conducted for the 100 largest cities around the globe. Climate and meteorological data as well as demographic data, general topography and geography (e.g., presence of large water bodies, elevation, latitude), managed landscape features such as urban biosphere reserves and large parks, building materials and building topography, and nature of the annual and seasonal weather patterns (e.g., prevailing winds, cloud cover, sunshine) are being compiled where possible. Analysis of the contributing factors to formation and persistence of the UHI is being identified. For a select number of cities (the size depending on availability of data and a preference for American cities and those with a similar composition), we are performing additional analyses focusing on landscape design from satellite data, and more in-depth analysis of topographic, geographic, and meteorological behavior.
A web site, The Atlas of Urban Heat Islands, will be designed to disseminate these findings and to provide an online resource for learning about UHIs around the globe and practical solutions for mitigating the effects of UHIs. This web site will include searchable maps and a comprehensive database of the data and findings from this study in an easy to access and readable format.
Develop a reduced complexity urban model to assess different design strategies for minimizing the effects of urban heat islands for a given city
A reduced-complexity urban model is being developed for the purpose of testing theories based on data analysis of urban heat islands and to explore options for mitigating the effects of urban heat islands in cities by modeling their behavior and altering landscape and building characteristics. A reduced-complexity model is a numerical model that simulates processes both mechanistically and empirically and simplifies processes for which processes can be represented well from observations. A benefit of reduced-complexity models is that the physics is simpler to represent in code and the model can perform many integrations very quickly without the need for exhaustive numerical processes for finding complex mathematical solutions.
The urban model will be designed to be compatible with the sometimes-limited input data that we expect will be available for use as inputs to the model. City representation will be based on an urban canyon approach whereby a city is composed of rooftops, shaded and sunlit walls, and a “canyon” floor (or city surface). The city surface comprises a fraction of impervious surfaces (e.g., roads, parking lots, sidewalks) and pervious surfaces (e.g., lawns, parks, lakes). Trapping of infrared radiation and reflection of solar radiation off of building surfaces will be represented as well as individual surface energy budgets. A multi-column heat conduction parameterization will be used to move heat through the boundary layer above the city.
Monitor the behavior of the Twin Cities urban heat island and develop mitigation strategies through modeling and sound engineering and landscape design practices
For this objective we are conducting two field experiments:
In the first experiment, we are establishing a dense array of temperature sensors around the Twin Cities Metropolitan Area that will be used to collect temperature data from over 200 sites every fifteen minutes over a four-year period. This data will be used to determine the daily, seasonal, and yearly behavior of the Twin Cities urban heat island. We are using this data to identify where the warming is strongest, and how the heat is transported by the winds to other regions in the metropolitan area – a behavior that occurs frequently in the wintertime. By identifying regions where the warming is the strongest, we are looking at the causes for the strong warming and practical mitigation strategies that can be adopted to reduce the urban warming.
In the second experiment, we are using net radiometers, temperature sensors, and infrared thermometers to measure the effects of various surface properties on the surface energy budget, an important driver of urban warming. By measuring incoming and outgoing solar and longwave radiation, and surface and air temperature at various heights, we will be able to quantify the contribution to heating of a surface before and after mitigation strategies are implemented (e.g., a roof before and after ‘greening’ with plants or light-colored paint). These experiments will be conducted both at the beginning of the project in order to establish the energy budget of different construction and landscape properties and near the end of the project after mitigation strategies have been identified.
Additionally, we will use the previously described urban model to simulate the urban heat island of the Twin Cities to identify the relative contributions of different factors influencing the urban heat island formation and persistence. In addition, we will conduct and analyze model simulations that reduce the urban heat island by 25%, 50%, and a theoretical 100% with the goal of identifying the engineering and economic feasibility of reducing the urban heat island of the Twin Cities through landscape design and building modification practices before irreversible changes are made.